<ul><li>Recent analysis reveals that open-source machine learning systems are highly vulnerable to security threats.</li><li>JFrog's report uncovers 22 vulnerabilities in 15 open-source ML projects, with threats targeting server-side components and privilege escalation within ML frameworks.</li><li>Specific vulnerabilities include the Directory Traversal flaw in Weave, compromising file access, and the access control issue in ZenML Cloud, enabling privilege escalation.</li><li>The findings highlight a gap in MLOps security and emphasize the need to integrate AI/ML security with broader cybersecurity strategies.</li></ul>

Open source machine learning systems are highly vulnerable to security threats

Discover more